Machine Learning and Deep Neural Networks
Many Machine Learning Techniques are most suited to solve real item problems and for gathering insights. Machine consists of supervised, unsupervised and reinforcement learning techniques. Suitable ML algorithms are applied based on the use case – Linear Regression, Logistic Regression, Decision Tree, SVM, Naïve Bayes, KNN, M-Means etc.
Artificial Neural Networks, Deep Neural Networks and Convolutional Neural Networks are modelled after the network formed by the neurons in the human body. They are based on the principle of conduction of nerve impulses from the source of stimulus to the brain centres for decoding and processing of the stimulus. Artificial Neural Networks use several layers of neurons that are fully interconnected, and propagation of information is controlled by weights.
Multi-Layer Perceptrons, Feed Forward Networks, Convolutional Neural networks, Recurrent Neural Networks, Long Short-Term Memory Networks are different types of neural networks used in several applications like image analytics/classification, Speech and Text Recognition, Univariate and Multivariate predictions etc.
Accord has successfully featured several applications with the extensive use of different types of Neural Networks. Techniques for optimization and compression of neural networks, network ensembling and transfer learning have been used for achieving best results.